We study the evolution of the price discovery process in the euro-dollar and dollar-yen currency pairs over a ten-year period on the EBS platform, a global trading venue used by both manual and automated traders. We find that the importance of market orders decreases sharply over that period, owing mainly to a decline in the information share from manual trading, while the information share of market orders from algorithmic and high-frequency traders remains fairly constant. At the same time, there is a substantial, but gradual, increase in the information share of limit orders. Price discovery also becomes faster, suggesting improvements in market efficiency. The results are consistent with theoretical predictions that with a lower information advantage, informed traders tend to use more limit orders.

We propose a new empirical specification of volatility that links volatility to the information flow, measured as the order flow in the market, and to the price sensitivity to that information. The time-varying market sensitivity to information is estimated from high-frequency data, and movements in volatility can therefore be directly related to movements in order flow and market sensitivity. Empirically, the model explains a large share of the long-run variation in volatility. Importantly, the time variation in the market's sensitivity to information is at least as relevant in explaining the persistence of volatility as the rate of information arrival itself. This may be evidence of a link between changes over time in the aggregate behavior of market participants and the time-series properties of realized volatility.

ABSTRACT We study the impact of algorithmic trading (AT) in the foreign exchange market using a long time series of high‐frequency data that identify computer‐generated trading activity. We find that AT causes an improvement in two measures of price efficiency: the frequency of triangular arbitrage opportunities and the autocorrelation of high‐frequency returns. We show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity. This result is consistent with the view that AT improves informational efficiency by speeding up price discovery, but that it may also impose higher adverse selection costs on slower traders. In contrast, the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence consistent with the strategies of algorithmic traders being highly correlated. This correlation, however, does not appear to cause a degradation in market quality, at least not on average.